Mastering Agentic AI Systems — Supplementals
Companion material for Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI
Exam. It was written primarily to support the NVIDIA NCP-AAI (NVIDIA Certified
Professional — Agentic AI) certification, and its breadth also makes it useful
preparation for AWS AIP-C01, Databricks Generative AI Engineer Associate,
Google Cloud Professional Machine Learning Engineer, and Microsoft AI-102. See
cert_mapping/README.md for exactly which chapters map to
which certification’s knowledge items.
If you’re studying for one of those exams, teaching a course that covers agentic AI
systems, or building your own curriculum on top of this material, this repository is
for you.
Repository contents
The book itself is organized into 10 Parts and roughly 94 chapters (see
cert_mapping/README.md for the authoritative chapter list).
Everything else in this repository is keyed to that chapter numbering.
| Folder |
What’s in it |
Study_Plan.md |
The full chapter text — the primary reading material. |
Prerequisite_Knowledge.md |
What to know before starting, and where to fill gaps. |
complexityCategories.md |
How topics are classified by difficulty across the book. |
quizzes_ver20JUN26.md |
Per-chapter quiz links (external, Google Forms). |
simulated_tests_ver20JUN26.md |
Full-length practice exam links (external, Google Forms). |
cert_mapping/ |
Chapter-to-certification knowledge-item mappings (H/M/L/N relevance ratings), one CSV per certification, plus source exam guides. |
labs/ |
Hands-on exercises. This folder is being rebuilt from scratch — see the note below. |
slides/ |
Presentation decks, one or more per chapter, PDF. |
videos/ |
Curated third-party YouTube resources, one file per Part. These are references, not contributed material. |
code_examples/ |
Standalone illustrative code snippets pulled from the chapter text. |
more_examples/ |
Additional worked examples. |
figures/ |
Diagrams and figures used in the book, one folder per chapter. |
References/ |
Source material and vendored third-party reference documentation. |
ai_tutor/ |
Resources for using an AI assistant as a study tutor, with example prompts. |
About labs/
The 17 files under labs/archive/ are prose excerpts from the
chapter text that happen to contain worked code — not finished, standalone
exercises, and moved out of the main labs/ listing on purpose so browsing
labs/ shows real labs and the template first. A real lab for every chapter is
still to be written. If you’re looking for hands-on practice today, start with
labs/LAB_TEMPLATE.md and the reference lab it links to, which
define what a finished lab looks like and are the standard new labs are built against.
How to use this material to study
- Start with
Prerequisite_Knowledge.md to check what background you need.
- Work through
Study_Plan.md chapter by chapter, using
cert_mapping/README.md to prioritize chapters most relevant to your target
certification.
- Use the per-chapter quiz links in
quizzes_ver20JUN26.md to check retention as you
go, and the full-length practice exams in simulated_tests_ver20JUN26.md closer to
exam day.
- Reinforce concepts hands-on with
labs/ (see the note above — coverage is still
being built out) and by reading the illustrative snippets in code_examples/.
- Use
slides/ for a condensed pass and videos/ for third-party explanations of
the same concepts from a different angle.
Licensing
As a work of the United States government, this project is in the public domain
within the United States. We also waive copyright and related rights in the work
worldwide through the
CC0 1.0 Universal public domain dedication.
All contributions to this project — prose, code, and otherwise — are released under
that same dedication. Third-party video links in videos/ are references and are not
themselves contributed material covered by this dedication. See
LICENSE.md for the full text.
Contributors
Recognized via the All Contributors specification,
which credits documentation, tutorial, video, content, review, ideas, and
maintenance work by name — not only code. Credit accrues as you contribute more; it
isn’t recomputed or reset. The bot integration that auto-updates this table from a
PR comment (@all-contributors please add @user for content) needs a one-time
GitHub App installation by a repository admin — until that’s done, entries are
added by editing .all-contributorsrc and running npx all-contributors generate
as part of the PR.
We’re looking for help
The biggest gap here is labs/: every chapter needs a real, hands-on lab written
from scratch — the 17 files under labs/archive/ are prose excerpts, not finished
exercises, and none of it counts as coverage. That’s the focus of this call, but
it’s not the only track. We’re also looking for instructional-content reviewers
(chapter text, slides, and quizzes — no coding required), help extending
cert_mapping/ to cover more certifications (one, Google’s “Generative AI Leader,”
already has its exam guide sitting here with zero mapping done), and help
verifying and growing the videos/ library, where a lot of entries in the later
chapters look populated but don’t actually link anywhere.
None of this is urgent-crisis territory — it’s the normal state of a project one
person built fast. See our call for collaborators for
the real numbers, what each track involves, and what you get out of it, and
CONTRIBUTING.md for how to get started.
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